Quantified Constraints Under Perturbation

Invariance of Fréchet frames under perturbation

Motivating the perturbations of frames in Hilbert and Banach spaces, in this paper we introduce the invariance of Fr'echet frames under perturbation. Also we show that for any Fr'echet spaces, there is a Fr'echet frame and any element in these spaces  has a series expansion.

Density estimation on small datasets

How might a smooth probability distribution be estimated, with accurately quantified uncertainty, from a limited amount of sampled data? Here we describe a field-theoretic approach that addresses this problem remarkably well in one dimension, providing an exact nonparametric Bayesian posterior without relying on tunable parameters or large-data approximations. Strong non-Gaussian constraints, w...

Adaptive Optimization with Constraints: Convergence and Oscillatory Behaviour

The problem of adaptive minimization of globally unknown functionals under constraints on the independent variable is considered in a stochastic framework. The CAM algorithm for vector problems is proposed. By resorting to the ODE analysis for analysing stochastic algorithms and singular perturbation methods, it is shown that the only possible convergence points are the constrained local minima...

Practical Constraints on Collaterals under Iranian Law

Nonlinear Viscosity Algorithm with Perturbation for Nonexpansive Multi-Valued Mappings

In this paper, based on viscosity technique with perturbation, we introduce a new non-linear viscosity algorithm for finding a element of the set of fixed points of nonexpansivemulti-valued mappings in a Hilbert space. We derive a strong convergence theorem for thisnew algorithm under appropriate assumptions. Moreover, in support of our results, somenumerical examples (u...